For CIOs, IT executives, and technology professionals, Strata's
Enterprise Big Data day lays out the roadmap to get your organization
up to speed on big data. In this all-day event, hear how to create
a big data strategy, understand the issues of managing data, and learn
how data science can be used powerfully in your organization.
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To kick off the Big Data for Enterprise IT Day, we present two views of big data. Is it truly something new, or just an evolution of what we have already? Join us for an interesting and entertaining talk that will help frame your thinking on big data.
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You can't manage what you don't measure, and that matters for big data initiatives too. This session will help you answer questions including: How far can big data take us from a business perspective? How do we compare our organization to others with respect to big data as a business enabler? How far can we push big data to transform our value creation processes?
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The promise of big data is to enable business transformation through new and powerful insights. Such transformations are mandated by the executive team, but how does that work with IT? What does big data success look like, and how do enterprises get there? Discover why big data is a business-owned problem, and how the relationship between IT and business must change.
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Machine learning and AI have appeared on the front page of the New York Times three times in recent memory. In this talk, Kaggle's president and chief scientist will explain exactly what occurred, why it was front-page newsworthy for the New York Times, how it will impact business, and what you need to know to make these new algorithms work for you.
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In this talk I will discuss the realities of human productivity bottlenecks in data analysis, and give an overview of research and product directions for addressing this critical bottleneck in a substantive way.
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Just the basics: you've probably heard about data mining and think you need a PhD to do it. Clever stuff with numbers. Predictions. Clusters. Algorithms. The 9 Laws explains the why of the basic steps you can take to be successful as a data miner, and show that this is primarily a business discipline, not a branch of computer science.
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